A framework and model detect mismatches between text and voice emotions in journaling using a controlled TTS dataset and asymmetric attention architecture, achieving macro-F1 of 0.711.
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PRISM learns shared sentiment prototypes to enable structured cross-modal comparison and dynamic modality reweighting in multimodal sentiment analysis, outperforming baselines on three benchmark datasets.
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I'm Fine, But My Voice Isn't: Cross-Modal Affective Dissonance Detection for Reflective Journaling
A framework and model detect mismatches between text and voice emotions in journaling using a controlled TTS dataset and asymmetric attention architecture, achieving macro-F1 of 0.711.
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Learning Shared Sentiment Prototypes for Adaptive Multimodal Sentiment Analysis
PRISM learns shared sentiment prototypes to enable structured cross-modal comparison and dynamic modality reweighting in multimodal sentiment analysis, outperforming baselines on three benchmark datasets.